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Psychiatry Research: Neuroimaging ∎ (∎∎∎∎) ∎∎∎–∎∎∎

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Psychiatry Research: Neuroimaging

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Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder

J. Paul Hamilton a,n, Gary H. Glover b, Epifanio Bagarinao c, Catie Chang d, Sean Mackey c, Matthew D. Sacchet e, Ian H. Gotlib f a Center for Social and Affective Neuroscience, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden b Department of Radiology, Stanford University, Stanford, CA, USA c Department of Anesthesiology, Stanford University, Stanford, CA, USA d Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA e Neurosciences Program, Stanford University, Stanford, CA, USA f Department of Psychology, Stanford University, Stanford, CA, USA article info abstract

Article history: Neural models of major depressive disorder (MDD) posit that over-response of components of the brain's Received 19 June 2014 salience network (SN) to negative stimuli plays a crucial role in the pathophysiology of MDD. In the Received in revised form present proof-of-concept study, we tested this formulation directly by examining the affective con- 8 January 2016 sequences of training depressed persons to down-regulate response of SN nodes to negative material. Accepted 14 January 2016 Ten participants in the real neurofeedback group saw, and attempted to learn to down-regulate, activity from an empirically identiﬁed node of the SN. Ten other participants engaged in an equivalent procedure Keywords: with the exception that they saw SN-node neurofeedback indices from participants in the real neuro- Major depressive disorder feedback group. Before and after scanning, all participants completed tasks assessing emotional Neurofeedback responses to negative scenes and to negative and positive self-descriptive adjectives. Compared to Functional magnetic resonance imaging participants in the sham-neurofeedback group, from pre- to post-training, participants in the real- Salience network Information processing biases neurofeedback group showed a greater decrease in SN-node response to negative stimuli, a greater decrease in self-reported emotional response to negative scenes, and a greater decrease in self-reported emotional response to negative self-descriptive adjectives. Our ﬁndings provide support for a neural formulation in which the SN plays a primary role in contributing to negative cognitive biases in MDD. & 2016 Elsevier Ireland Ltd. All rights reserved.

1. Introduction response to positive relative to neutral stimuli in MDD (Hamilton et al., 2012). Based on these findings, we presented a neural Over the last two decades, neuroimaging investigations of account of the well-documented heightened response to negative Major Depressive Disorder (MDD) have been instrumental in stimuli in MDD, which has been hypothesized to play a significant increasing our understanding of this prevalent and debilitating role in the etiology and maintenance of this disorder (Beck, 1976; condition (Kessler and Wang, 2009). Sufficient data have now Gotlib and Joormann, 2010). In this formulation, we posit that accumulated from functional neuroimaging investigations of through monosynaptic projections to the amygdala, dACC, and depression that, through meta-analytic integration, we have been fronto-insular cortex (Jones and Burton, 1976; Mufson and Mesu- able to identify the neural abnormalities that have been found lam, 1984; Padmala et al., 2010), heightened baseline activity in most reliably to characterize this disorder. Specifically, in a recent the pulvinar nucleus in depression (Hamilton et al., 2012) potentiates response of these primary limbic nodes to affective meta-analysis of studies using task-based functional magnetic information. resonance imaging (FMRI), we found reliably increased response In this model, fronto-insular cortex, dACC, and amygdala — in fronto-insular cortex, amygdala, and dorsal anterior cingulate primary nodes in the brain’s salience network (SN), which is cortex (dACC) to negative stimuli relative to neutral stimuli; postulated to undergird perception of and response to personally importantly, we did not observe this pattern with respect to relevant stimuli (SN; Seeley et al., 2007) — play a crucial role in biasing the processing of negative information in MDD. This and n Corresponding author. similar formulations proposed by clinical neuroscientists (e.g., E-mail address: [email protected] (J.P. Hamilton). Menon, 2011) are difficult to test directly using traditional http://dx.doi.org/10.1016/j.pscychresns.2016.01.016 0925-4927/& 2016 Elsevier Ireland Ltd. All rights reserved.

Please cite this article as: Hamilton, J.P., et al., Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder. Psychiatry Research: Neuroimaging (2016), http://dx.doi.org/10.1016/j.pscychresns.2016.01.016i 2 J.P. Hamilton et al. / Psychiatry Research: Neuroimaging ∎ (∎∎∎∎) ∎∎∎–∎∎∎ functional neuroimaging paradigms, which typically identify only 2. Methods and materials neural correlates of cognitive or emotional activity. Given this limitation of traditional FMRI paradigms in making causal attri- 2.1. Participants butions, in the present study we used an FMRI-based neurofeed- Twenty-two adults diagnosed with MDD initially participated in this study. All back system that allows individuals to see and learn to modulate participants met criteria for a DSM-IV diagnosis of MDD based on their responses to regional brain responses. The implementation of this method the Structured Clinical Interview for DSM (SCID; First et al., 2001), administered by permits investigators to examine the effects on behavior of mod- trained diagnostic interviewers. Depressed individuals with a current comorbid ulating neural activation (Weiskopf et al., 2004), as opposed to the diagnosis of any Axis-I disorder other than Social Anxiety Disorder (SAD) were not included in the study. Given that we were comparing two groups of depressed more typical examination of the effects on the brain of manip- participants (real versus sham NFT), we included depressed individuals who were ulating behavior. Such FMRI neurofeedback systems have now taking antidepressant medication in this study because we would not be con- been used successfully to teach healthy individuals to volitionally founding medication status with psychiatric diagnosis and, importantly, because this would bolster the generalizability of our findings to the general population of alter response in sensorimotor (DeCharms et al., 2004), and limbic depressed persons, over half of whom take psychotropic medications (Pratt et al., regions (Caria et al., 2007; Hamilton et al., 2011), and to reduce the 2011). At the end of the interview session, all participants completed the Beck experience of pain (deCharms et al., 2005). Depression Inventory-II (BDI-II; Beck et al., 1979) and the Positive and Negative Recent studies using FMRI neurofeedback in MDD have Affect Schedule (PANAS; Watson et al., 1988). The BDI-II and PANAS are frequently used and well validated self-report measures of the severity of depressive symp- explored the therapeutic utility of this method in depression. A toms and levels of positive and negative affect, respectively. All stages of the re- seminal, non-placebo-controlled study showed that it is possible search presented here were carried out in accordance with The Code of Ethics of to teach depressed persons to increase idiographic neural activity the World Medical Association (Declaration of Helsinki) for experiments involving humans. associated with positive affect and, in doing so, decrease depres- To assess cognitive, behavioral, and neural effects of NFT, we assigned partici- sive symptomatology (Linden et al., 2012). Another recent study pants to one of two groups: REAL, in which participants saw real-time neural showed that teaching depressed persons to increase amygdala response data from a component of their own SN, and SHAM, in which participants saw, in the context of an otherwise equivalent neurofeedback procedure, neural activity during recall of happy autobiographical memories response data from participants in the REAL group instead of their own neural data. increases happiness and decreases anxiety in MDD (Young et al., We elected to use a sham NFT control group—as opposed to a control group seeing 2014). While not controlling for placebo effects, these studies have neurofeedback from a control region not implicated in depression—because this form of experimental control is the only way to ensure that the positive and ne- provided strong preliminary support for the clinical efficacy of gative reinforcement provided from neurofeedback, and the potential effects of this FMRI neurofeedback paradigms. feedback on subsequent task performance, were precisely controlled. Importantly, In the current proof-of-concept study, we take a different per- two researchers were involved in NFT scanning: one who interacted with partici- spective relative to previous, clinically oriented work by using pants and was blind to group assignment and one that ran the NFT interface and who was not blind to group assignment. Further, in keeping with the objective of FMRI neurofeedback as a tool to test and develop neural models of this study was to investigate the role of the SN in the pathophysiology of MDD, we MDD. For this study, we constructed an experimental paradigm for focused only on the effects of learned modulation of this network. testing the role of SN node over-response in producing the nega- Given pilot data indicating that 5 out of 6 individuals were successful in using neurofeedback to learn how to control neural response, we first ran 12 participants tive affective biases implicated reliably in MDD. In designing this through the REAL neurofeedback protocol and identified 10 who were successful study, we reasoned that if the SN plays a crucial role in producing in using NFT to learn to regulate responding of a functionally defined SN-node (we negative affective biases in MDD, then teaching depressed persons include performance data for the two non-learners in a supplement to this article). Even though we excluded only a small proportion of our recruited REAL NFT to decrease responding in SN nodes to negative affective sample, to ensure that this process did not bias our sample (i.e., that selecting information should decrease affective responding to negative but successful neuromodulators did not inadvertently also select for factors such as age not positive information. If, on the other hand, learned down- or severity of depression that might affect performance), we selected and ran modulation of SN response to negative information has no effect through the SHAM NFT protocol depressed individuals who were matched to participants from the REAL group with respect to age, education, medication, on response to negative information, the hypothesis that the SN symptom severity, and comorbidity with SAD. Importantly, all participants pro- plays a critical role in negative affective biases in MDD will be vided consent for participation in the NFT procedure knowing that there would be disconfirmed. a chance that they would see sham neurofeedback. To measure the effects of SN-node neurofeedback training (NFT), we assessed responses to both negative and positive stimuli 2.2. Neurofeedback scanning protocol before and after a regimen of NFT. In order to determine effects on 2.2.1. Overview affective functioning attributable to NFT as distinct from placebo Participants were first shown the neurofeedback interface and neuromodula- effects, we also included a group of depressed participants who tion task outside of the scanner. In addition, before and after scanning, participants received sham NFT; that is, the neurofeedback they received was completed two computer-based tasks (described below) to assess response to not veridical but, instead, was feedback from other depressed affective challenge. After entering the scanner, participants underwent a functional-localizer/pre-training assessment scan, three NFT scans, and a post-training participants who had received real neurofeedback. We predicted, assessment scan. Finally, following scanning and assessment, all participants were first, that receiving real NFT would lead to successful down- interviewed about whether they believed the NFT signal was real and, for ex- modulation of SN node response; thus, we predicted that, com- ploratory purposes, what technique they used to control the NFT signal. pared with depressed participants who received sham NFT, depressed participants who received real NFT would show 2.2.2. Pre- and post-scanning assessments To assess changes in affective responding due to NFT, participants completed, reduced response of their most reactive SN node to negative sti- both before and after training, an out-of-scanner picture-rating task in which they muli following NFT. Further, and in accord with the model we viewed and rated on a scale of 1–9 the intensity of 15 novel, negatively valenced present above, we predicted that, relative to their sham NFT pictures from the International Affective Picture System (IAPS; Lang and Green- wald, 1993). We used a total of 78 novel, negative IAPS pictures (mean intensity: counterparts, depressed persons who received real SN NFT would 4.18; SE: 0.17; mean arousal: 5.11; SE: 0.15) for the behavioral testing and scanning also exhibit reduced affective responding to negative affective portions of the study (15 for each rating task, 15 for each of the pre- and post- challenge. Finally, given that the SN has been conceptualized as training scans, and 6 for each of three NFT scans). To ensure their appropriateness part of a negative valence system (Insel et al., 2010) in MDD for use with depressed participants, the negative pictures were all rated by two trained clinicians on a 1–9 scale as reflecting higher levels of sadness than of fear or (Hamilton et al., 2012), we predicted that the effects of real NFT disgust (mean sadness: 7.5; mean fear: 2.5; mean disgust: 2.0). In addition, before would not generalize to affective responses to positive stimuli. and after NFT, participants completed a brief version of the self-referent encoding

the ﬁrst two TRs (o1–o2) following picture onset, divided by the average of o1 and

2.2.5. Prescan preparation o2 (see Fig. 1 for a depiction of the trial structure and calculation of SN-node Prior to scanning, participants were introduced to the neurofeedback interface response for NFT scans). and the task design of the study. They were told that they would see a neurofeedback signal from a brain structure involved in emotional response and that 2.2.8. Post-training assessment scan they should try to adopt strategies that brought about a decrease in the response of Following the three NFT scans, participants engaged in a final scan equivalent this structure. Consistent with previous research (Caria et al., 2007), the results of in structure to the pre-training scan with the exceptions that novel IAPS pictures pre-testing indicated that NFT with the SN progressed slowly if participants were were used and that upon seeing the negative pictures, participants were asked to not given a set of possible strategies. Therefore, at the outset of scanning, we invoke the affective regulatory strategy that had proven most effective in reducing provided participants in both the REAL and SHAM groups with strategies involving the neurofeedback signal they saw during NFT runs; no neurofeedback signal was cognitive reappraisal (Ochsner et al., 2002), attentional redirection (Kalisch et al., provided during this post-training scan. 2006), and imagery (Jarvinen and Gold, 1981) to try at their discretion (see Sup- plement to Section 2). Further, participants were reminded that the neurofeedback signal was there to help teach them, and that if one strategy did not produce the 2.3. Analysis desired change in the neurofeedback signal, they should try another. 2.3.1. ROI and behavioral data 2.2.6. Functional-localizer/pre-training assessment scan Given that participants were trained using the neurofeedback index described For their first functional scan—designed both to identify a region of interest above, we used this index as the primary dependent variable for assessing the (ROI) for NFT (REAL group) and to assess pre-training response in this ROI (REAL effects of NFT on SN-node response. We first calculated for each participant the and SHAM groups)—participants engaged in 15 trials of viewing negative IAPS average response across trials of the SN ROI for the pre-training and post-training pictures. Participants were instructed to attend to and stay focused on each picture scans and converted these data to a single percent-change index: for the six seconds that it was presented and then to engage in an active control ((post − training response – pre − trainingresponse ) / ( pre − trainingresponse ))* 100 task for 16 s following the offset of each picture. Given that response in neural regions that subserve affect can persist beyond the duration of an affective chal- We then compared this index of SN-node response change in the REAL and lenge (Siegle et al., 2002), we used a simple, active control task—pressing a dif- SHAM groups. Similarly, for ratings of the intensity of the IAPS pictures and of the ferent button (1–4) every two seconds on an MR-compatible keypad—to distract self-relevance of the negative and positive adjectives that were obtained before and participants from any persisting, ruminative thoughts following the offset of after NFT training, we calculated percent-change scores and examined differences affective stimuli. Our goal in administering this control task was to make analysis in these scores between participants in the REAL and the SHAM groups. Given our a of BOLD time-series data more tractable by facilitating deactivation in neural priori directional predictions, we used one-tailed t-tests to compare the REAL and

Fig. 1. Trial structure and calculation of SN-node response for NFT scans.

SHAM groups. Further, we calculated effect sizes of NFT effects (Cohen, 1988). cause and effect relations between NFT strategies and ROI activity. Finally, given that motion can affect BOLD signal, to ensure that participants in the Finally, the REAL and SHAM groups did not differ in pre- to post- REAL relative to the SHAM group were not learning to move in order to inﬂuence their neurofeedback signal, we also compared the REAL and SHAM groups on NFT either in absolute motion or in the correlation between mo- percent-change indices of the Euclidean norm of the derivatives of their motion tion and task timing (both p40.15). covariates and on percent change of correlations between motion and the task timing. 3.3. Affective responses

2.3.2. Functional connectivity Compared with the SHAM NFT group, the REAL NFT group To examine whether depressed participants in the REAL group, relative to participants in the SHAM group, recruited additional brain regions in the service of showed a greater decrease from pre- to post-NFT in emotional learning to modulate their respective NFT ROIs, we conducted a functional con- response to negative IAPS pictures (po0.05; Cohen's d¼0.78; see nectivity (FC) analysis—via a procedure described elsewhere (Hamilton and Gotlib, Fig. 2C) and in ratings of self-relevance of the negative adjectives — 2008) using as the seed region each participant's ROI. Following normalization of (p¼0.06; Cohen's d¼0.73; see Fig. 2D); the two groups did not each participant's FC coefficient maps to standard space (Talairach et al., 1988)we conducted a voxel wise, mixed model, two-by-two analysis of variance (ANOVA) of differ in the change from pre- to post-NFT in their ratings of the Group (REAL versus SHAM) repeated over Training (PRE versus POST NFT) using a self-relevance of the positive adjectives (p40.15; p4.15; Cohen's family-wise error corrected α¼.05. Importantly, this analysis was conducted for d¼0.24). In neither group did changes in IAPS or self-relevance exploratory purposes and, given the small sample size and the degrees of freedom ratings correlate significantly with changes in SN response. See used in the two-by-two factorial model, was relatively insensitive to detect effects. Supplemental Fig. 2 for performance data from the REAL and SHAM groups in addition to the two EXCLUDED participants. 3. Results 3.4. Functional connectivity 3.1. Demographic and clinical measures Our FC analysis identified no regions that changed connectivity As shown in Table 1, the REAL and SHAM groups did not differ with participant ROIs as a function of NFT training in the REAL significantly with respect to age, years of education, depressive group relative to the SHAM group. To augment this analysis, we symptomatology, or levels of negative or positive affect (all t-tests also calculated at all voxels the degree of response to the emotion p40.15); further, the two groups also did not differ significantly in regulation task in all participants for both the pre- and post- the proportion of participants who had comorbid Social Anxiety training scans and then entered these estimates into a two- Disorder or who were taking psychotropic medications (all chi- (Group) -by-two (Time) factorial analysis of variance. See Sup- square p40.15). plemental Fig. 3 for the results of this analysis.

ﬁ 3.2. Neuromodulation 3.5. Post NFT and assessment debrie ng

We present in Fig. 2A. the loci of the SN-node ROIs—both in All participants from both the REAL and SHAM groups believed Talairach coordinates and in the context of a brain map—for each that the NFT signal they saw was a real NFT signal from their own of the ten REAL neurofeedback participants. The SN region brain. showing the strongest response during the localizer/pre-training assessment scan was typically in fronto-insular cortex (8 of 10 participants); two participants, however, showed the highest re- 4. Discussion sponse in the dACC. For comparison purposes, we also identified in the SHAM group the component of the SN that was most re- The present study was designed to test the hypothesis that sponsive during the pre-training scan; as in the REAL group, 8 of the SN plays a critical role in the negative affective bias reliably the 10 SHAM participants showed the strongest response in associated with MDD (Beck, 1976; Gotlib and Joormann, 2010). To fronto-insular cortex and two participants showed the greatest test this hypothesis, we examined whether learning to control response in the dACC (Talairach coordinates presented in Fig. 2A.). reactivity of nodes of the SN using NFT during negative affective As expected, participants in the REAL NFT group exhibited a provocation affected emotional response in MDD. Consistent with greater decrease in SN-node ROI response from pre- to post-NFT our hypotheses, we found that compared with depressed persons than did participants in the SHAM NFT group (p¼0.06; Cohen's who received SHAM NFT, depressed persons who received REAL d¼0.70; see Fig. 2B). More specifically, REAL and SHAM groups did NFT learned to control reactivity in components of the SN and, as a not differ with respect to ROI response before NFT (p40.10), ROI result of NFT, showed decreased emotional response to negative, response did not change from pre- to post-NFT in the SHAM group but not to positive, stimuli. These findings have important impli- (p40.10), but ROI response did decrease significantly in the REAL cations for neural conceptualizations of depression. NFT group (po0.05). Importantly, in none of the three NFT runs Theorists have postulated that cognitive biases facilitating the did the REAL and SHAM groups differ with respect to ROI processing of negative material in depression contribute to the response; this is perhaps not surprising, however, given that etiology and maintenance of this disorder (Beck, 1976; Gotlib et al., several participants reported trying both to increase and decrease 2004). In a synthesis of functional neuroimaging investigations of the NFT signal during NFT runs in order to get a better sense for MDD, we identified a small number of structures showing reliable

Table 1 Group demographic and clinical data.

Sample size Age Years education Proportion receiving psycho- Proportion with comorbid so- BDI-II PANAS-Negative PANAS-Positive tropic medication cial anxiety disorder

REAL 10 31.2 (72.9) 16.4 (70.83) 60% 30% 33.3 (72.3) 36.7 (73.8) 25.1 (72.1) SHAM 10 34.5 (73.7) 17.0 (70.75) 40% 20% 34.6 (74.0) 31.3 (73.6) 22.8 (72.5)

Note: all between-group comparison p4.15; BDI¼Beck Depression Inventory; PANAS¼Positive and Negative Affect Schedule

Fig. 2. A. Loci of functionally defined SN-node ROIs for the REAL (brain map and Talairach coordinates) and SHAM (Talairach coordinates) NFT groups. B. Change from pre-to post-NFT in SN-node ROI response in REAL and SHAM groups. C. Change from pre-to post-NFT in self-reported response to negative IAPS pictures in REAL and SHAM groups. D. Change from pre-to post-NFT in self-reported response to negative self-descriptive adjectives in REAL and SHAM groups. over-response to negative stimuli in MDD and postulated that, relatively small sample. While we observed large effects of NFT among these, the nodes of the SN play a primary role in con- both on changes in SN node responding and on responses to tributing to negative processing biases in depression (Hamilton negative affective challenge—with Cohen's d scores all .70 or et al., 2012). In the present study we showed that teaching higher (Cohen, 1988)—it will be important in subsequent work to depressed individuals to decrease SN-node reactivity leads to a examine NFT effects in larger depressed samples. Further, we reduction in emotional reactivity to negative stimuli. Importantly, hasten to point out that our objectives in this study were to we found no evidence of involvement of non-SN structures in this examine short-term neural and behavioral changes resulting from SN-NFT effect. These findings, considered alongside the putative NFT; thus, the data we present are equivocal with respect to any functional role of the SN (Seeley et al., 2007), are consistent with long-term effects of learning to modulate the SN. Finally, based on the formulation that nodes of the SN play a key role in negative results from early piloting that healthy males were relatively processing biases in MDD. It is important that we note here that ineffective at learning to control neurofeedback signals from participants learned to control as opposed to reduce NFT ROI regions involved in affective responding, we recruited only female response given that several participants reported trying both MDD participants for this study; our results, therefore, my not to decrease and increase NFT reactivity during the training runs. generalize to the full population of individuals with MDD. The neural and behavioral effects of NFT, therefore, may be best While we designed this study as a critical test of the hypothesis conceptualized as resulting from general mastery of ROI response that SN over-response plays a vital role in negative affective biases as opposed simply from learning to decrease ROI reactivity. in MDD, the present results do have potential implications for We should note four limitations of the present study. First, we non-pharmacological neural interventions for depression. Specifi- did not include a healthy control group in this study that could cally, along with recent and more clinically oriented work (Linden allow us to replicate the documented finding of heightened SN et al., 2012; Young et al., 2014) our findings indicate that NFT may response to negative affective challenge in MDD. Rather, based on be a useful therapeutic tool for depression. In this context, how- meta-analytic results presented above (Hamilton et al., 2012), we ever, it is important to acknowledge that we did not examine the assume here that elevated response to negative stimuli in nodes of effects of NFT on depressive symptomatology, but instead, given the SN is reliable in MDD. Second, like recent preliminary investi- our formulation that SN over-response affects acute but not gations of neurofeedback effects in MDD (Linden et al., 2012; chronic emotional expression in MDD, we assessed its effects on Young et al., 2014), our proof-of-concept investigation of SN NFT affective response. Subsequent investigations should test this for- effects on negative processing biases in MDD incorporated a mulation more explicitly by examining the effects of multiple NFT

Please cite this article as: Hamilton, J.P., et al., Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder. Psychiatry Research: Neuroimaging (2016), http://dx.doi.org/10.1016/j.pscychresns.2016.01.016i 6 J.P. Hamilton et al. / Psychiatry Research: Neuroimaging ∎ (∎∎∎∎) ∎∎∎–∎∎∎ sessions on mood and diagnostic status in MDD, and by assessing Glover, G.H., Law, C.S., 2001. Spiral-in/out BOLD fMRI For increased SNR and the effectiveness of NFT as a supplement to, or in lieu of, more reduced susceptibility artifacts. Magn. Reson. Med. 46, 515–522. Gotlib, I.H., Joormann, J., 2010. Cognition and Depression: Current Status and Future traditional cognitive-behavioral therapies for depression. Finally, Directions, pp. 285–312. given that FMRI-based neural interventions have a number of Gotlib, I.H., Krasnoperova, E., Yue, D.N., Joormann, J., 2004. Attentional biases for exclusion criteria (e.g., claustrophobia, weight restrictions) it will negative interpersonal stimuli in clinical depression. J. Abnorm. Psychol. 113, – be important in future work to identify peripheral biomarkers of 127 135. Hamilton, J.P., Etkin, A., Furman, D.J., Lemus, M.G., Johnson, R.F., Gotlib, I.H., 2012. effective neural regulation (e.g., changes in electroencephalo- Functional neuroimaging of major depressive disorder: a meta-analysis and graphic or cardiac measures) for use in more traditional biofeed- new integration of baseline activation and neural response data. Am. J. back paradigms. Psychiatry, 693–703. Hamilton, J.P., Glover, G.H., Hsu, J.J., Johnson, R.F., Gotlib, I.H., 2011. Modulation of subgenual anterior cingulate cortex activity with real-time neurofeedback. Hum. Brain Mapp. 32, 22–31. Acknowledgments Hamilton, J.P., Gotlib, I.H., 2008. Neural substrates of increased memory sensitivity for negative stimuli in major depression. Biol. 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